Harrison Et Al. - 2012 - Hospital Volume and Patient Outcomes After Cholecy

7/31/2019 Harrison Et Al. - 2012 - Hospital Volume and Patient Outcomes After Cholecy

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Hospital volume and patient outcomes aftercholecystectomy in Scotland: retrospective, nationalpopulation based study

OPEN ACCESS

Ewen M Harrison lecturer in surgery1, Stephen ONeill research fellow

1, Thomas S Meurs medical

student2, Pang L Wong medical student

1, Mark Duxbury clinician scientist and honorary consultant

surgeon1, Simon Paterson-Brown consultant surgeon and honorary senior lecturer

1, Stephen J

Wigmore professor of transplantation surgery1, O James Garden regius professor of clinical surgery

1

1Department of Clinical Surgery, University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK; 2Department of Surgery, LeidenUniversity Medical Centre, Leiden, Netherlands

Abstract

Objectives To define associations between hospital volume and

outcomes following cholecystectomy, after adjustmentfor case mix using

a national database.

Design Retrospective, national population based study using multilevel

modelling and simulation.

Setting Locally validated administrative dataset covering all NHS

hospitals in Scotland.

Participants Allpatients undergoing cholecystectomy between 1 January

1998 and 31 December 2007.

Main outcome measures Mortality, 30 day reoperation rate, 30 day

readmission rate, and length of stay.

Results We identified 59 918 patients who had a cholecystectomy in

one of 37 hospitals: five hospitals had high volumes (>244

cholecystectomies/year), 10 had medium volumes (173-244), and 22

had low volumes (


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in processes, such as the failure to rescue patients from

complications.6

Within this debate, less attention has been given to high volume,

general surgical procedures with a low risk, possibly because

robust outcomes in administrative databases are harder to

identify. In addition, since measureable outcomes (such asmortality) are better after surgery of this type, effect sizes tend

to be small and could be regarded as less clinically relevant.

Yet, in view of the large number of cholecystectomies performed

in developed countries each year, even relatively rare adverse

events contribute considerably to morbidity. Indeed, a study

examining the contribution of different surgical procedures to

total inpatient morbidity ranked inpatient cholecystectomy third

(6%) after colectomy and small bowel resection.7

Despite this, the relation between hospital volume and

cholecystectomy remains unclear. In a historical study of open

cholecystectomy, no link was shown between hospital volume

and mortality.8 Similarly, in a large contemporary series of

patients undergoing laparoscopic cholecystectomy in the UnitedStates, researchers found no association between hospital volume

and the risk of major complication or death, although open

conversion was associated with low volume centres.9 In the

acute setting where complications are more common and

outcome differences can be more prominent, higher volume

surgeons were associated with shorter lengths of stay and fewer

open conversions,10 but again no association with hospital

volume was shown.

Using a high quality national dataset which encompasses all

emergency and elective surgical procedures, we aimed to

evaluate four independent measures of patient outcome after

cholecystectomy and determine their association with hospital

volume. We characterised this link further with risk modelling

to determine the effect of age, comorbidity, and socioeconomic

status.

Methods

Study design, setting, and participants

We did a retrospective, population based study using data from

the Information Services Division of NHS Scotland. We

identified all NHS patients undergoing cholecystectomy between

1 January 1998 and 31 December 2007 in Scotland. Episode

records including all previous and subsequent encounters were

retrieved up to 31 December 2008 (web appendix, page 1).

We excluded patients from further analysis if the diagnosis

relating to the index procedure was related to carcinoma or

trauma (web appendix, page 2), or if the index procedure was

carried out in a paediatric or private hospital (web appendix,

page 3). We accounted for hospitals amalgamating or changing

name during the study period.

The study was performed in accordance with the Strengthening

the reporting of observational studies in epidemiology

(STROBE) guidelines.11 The National Services Scotland Privacy

Advisory Committee approved the study. All patient data were

anonymised. The primary investigator is a registered data

controller with the United Kingdom Information

Commissioners Office and all data were treated in accordance

with the principles of the Data Protection Act 1998.

Data validation and bias

The Information Services Division database was internally

consistent and we did no data imputation (missing data related

only to deprivation scoring). We matched a three year sample

with a centrally administered, operating theatre database, and

the concordance of matched cases was 89%. The coding of

laparoscopic to open conversions was inconsistent in earlier

years, and consequently we classified all these procedures as

open. The identities of the operating and responsible surgeons

were poorly correlated (50% concordance) and were not suitable

for surgeon volume analysis. In a second analysis, we matchedcases to a locally held audit database and saw a good correlation

for reoperation rate (98%), readmission rate (97%), and length

of stay (94%).

Factors and covariates

We defined hospital volume as the mean number of

cholecystectomies performed per hospital per year in 1998-2007.

In a manner similar to Birkmeyer and colleagues, we evaluated

hospital volume as a continuous (log transformed) variable in

the assessment of statistical significance. We then created

categorical variables by ranking institutions in order of

increasing volume and selecting cut-off points that most closely

sorted patients into three evenly sized groups with low, medium,andhigh volume (web appendix 3).3 No hospitals were excluded.

We did sensitivity analyses using different cut-off points and

an alternative 10 year cohort (1997-2006), from which we saw

no changes in the significance of model parameter estimates for

differences between hospital volume groups.

We explored models of morbidity scoring using disease codes

from ICD-9 and ICD-10 (international classification of diseases,

9th and 10th revisions) from all previous healthcare encounters,

including the Deyo modification of the Charlson score and the

Elixhauser score.12 No single method resulted in better model

performance, and Charlson score was used in the final analysis

(web appendix, page 3).3 The Scottish Index of Multiple

Deprivation (SIMD) 2009 uses an improved methodology to

provide a relative measure of deprivation in Scotland. Patients

postcodes at index procedure determined SIMD quintiles, which

were considered as a continuous variable. An SIMD score could

not be assigned for 271 patients, because they were not included

in models using SIMD. We found no significant patterns

between these 271 patients and the remainder of the cohort for

other variables or outcome measures.

Outcome measures

We determined patient death by using probability matching,

record linkage procedures between patient episodes from the

Information Services Division and by using death records from

the Registrar General of Scotland. Mortality was defined as

death occurring within 30 days of the index procedure or before

discharge (censored at 120 days). We defined 30 day reoperation

as the occurrence of any procedure in the 30 days after index

cholecystectomy involving the upper digestive tract (Office of

Population Censuses and Surveys version 4, code G) or other

abdominal organs (principally digestive; code J). We defined

30 day readmission as an emergency readmission to any Scottish

hospital within 30 days of the date of the index procedure.

Length of stay was defined as the period from the date of index

procedure to the discharge date of continuous inpatient stay

(that is, a patient who was directly transferred to another hospital

was not classified as being discharged).

Statistical proceduresWe examined initial univariable associations using 2 tests and

one way analysis of variance for categorical and continuous

predictors, respectively. All P values were two tailed. We

initially specified binary logistic regression models

conditionally, using backwards likelihood ratio methods.

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BMJ2012;344:e3330 doi: 10.1136/bmj.e3330 (Published 23 May 2012) Page 2 of 14

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However, in all included models, factors and covariates found

to be significant in univariable models were also significant in

multivariable models. We determined goodness of fit and

included the Hosmer-Lemeshow testand predictive performance

quantified by the area under the receiver operator characteristic

curve (c statistic). The ratio of predictors to events was neverlower than 15:1.

We used bootstrap methods to derive confidence intervals in

multivariable models unless otherwise indicated. We specified

hierarchical logistic regression models to account for the

clustering of patients within hospitals. In all cases, random

coefficient models were not significantly better than random

intercept models (as determined by likelihood ratio tests). For

mortality outcomes, the multilevel model was no improvement

on the fixed effects model; however, since death after

cholecystectomy was regarded as a rare event, we used a model

including a bias correction.13 Length of stay in cholecystectomy

was particularly right skewed, making analysis difficult. The

problems of ordinary least squares and regression methods, even

using a log transformed dependent variable, are well described.14

We used a Cox proportional hazards procedure to model the

risk of discharge. The underlying hazard function was assessed

and seen to be constant, and no time dependent variables were

specified. Page 8 of the web appendix shows a generalised linear

model for comparison.

Finally, to provide a real world interpretation of the data, we

simulated predicted probabilities and absolute risk differences

fordifferent risk strata using the fixed effect models (asymptotic

normal approximation to the log likelihood, 1 000 000

simulations per quantity of interest; web appendix, page 4). 15

In particular, these procedures rely on the correct specification

of interactions between variables. We assessed all two way

interactions and identified no significant interactions in the final

models.

We specified the logistic regression models in Stata SE 11.0

(StataCorp) using commands logit and xtmelogit. We did rare

event logistic regression (relogit16), generalised linear modelling,

Cox proportional hazards modelling, and risk modelling in R

2.11.1 (R Foundation for Statistical Computing) using the Zelig17

and Survival packages.

Results

We identified 60 732 individuals as having undergone a

cholecystectomy between 1 January 1998 and 31 December

2007. Patients were excluded if the primary diagnosis was cancer(263) or trauma (12) or if the cholecystectomy was performed

in one of eight private hospitals (457) or one of four paediatric

hospitals (82), giving a final dataset of 59 918 patients and 37

hospitals. Patients were equally divided across three bands of

hospital volume: high volume (>244 procedures/year; five

hospitals, 18 425 patients), medium volume (173-244; 10, 20

534), and low volume (


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between low and high volume groups became more pronounced

(0.0030, 0.0007 to 0.0059, P=0.010; 338, 171 to 1491).

As baseline risk increased, absolute risk differences became

highly clinically significant (fig 2, right panel). Table 5 provides

examples of patients with different risk profiles. For instance,

in example 7, a man older than 70 years with significantcomorbidity presenting as an emergency and undergoing

cholecystectomy has a 15-20% probability of death. At this

level of risk, differences between hospital volume bands were

pronounced, as shown by the numbers needed to treat to harm

(low v high volume comparison 17, 95% confidence interval

nine to 74; medium v high volume 19, 10 to 143; table 5).

Reoperation and readmission rates at 30 days

The association between rates of reoperation and readmission

and hospital volume was non-linear. The medium volume group

had a greater number of reoperations than the low and high

volume groups (4.65% v 3.23% and 3.29%, respectively;

P


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procedures including open (n=7113) and laparoscopic (n=8602)

cholecystectomy, and found no significant association between

hospital procedure volume and 30 day mortality rate. 18 Khuri

and colleagues selected cohort differed from our cohort by

being older and predominately male, with the major difference

in ranges of annual hospital volume: open cholecystectomy1-39, laparoscopic cholecystectomy 0-44. All these centres

would have been classified as low volume in the current study,

making meaningful comparisons difficult.

Similarly, a smaller uncontrolled Norwegian study (n=5343)

found a linear association between hospital volume and a severe

complications index, but again, mean hospital volume was

significantly lower (>50, 25-50, and 15

cholecystectomies/year) were associated with significantly

decreased risk of a prolonged length of stay (odds ratio 0.91,

P=0.022) and reduced risk of open conversion (0.68,

P


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What is already known on this topic

Associations between hospital volume and outcome after specialist surgery are well defined and have guided healthcare servicereconfiguration for the benefit of patients

The effect of hospital volume on outcome after low risk, high volume procedures (such as cholecystectomy) is unclear

Smaller studies have shown a weak link between increased hospital volume and reduced mortality after cholecystectomy

What this study adds

Highvolume centres havelower mortality, shorter lengths of stay, and reduced ratesof reoperation and readmission after cholecystectomy.Although these relative risk differences are significant, they are irrelevant for most patients with a low baseline risk

For high risk patients, particularly those who are elderly or with comorbidity, hospital choice might be important even for electiveprocedures

Differences in hospital structures and processes should be examined; centralisation of care for higher risk patients could improveoutcomes after cholecystectomy

providing access to the data and invaluable advice, and Olivia Swann

for critically reviewing the manuscript.

Contributors: EMH is the guarantor and takes responsibility for the

integrity and accuracy of these data and the final decision to submit for

publication. EMH participated in the study design; data collection,processing, analysis, and interpretation; manuscript writing; and

construction of tables and figures. SON participated in the systematic

review, data interpretation, and manuscript writing. TM participated in

the study design; data processing, analysis, and interpretation; and

manuscript writing. PLW participated in data validation and manuscript

writing. MD, SP-B, SJW, andOJG participatedin thestudydesign, data

interpretation, and manuscript writing. The authors had full access to

all data in the study.

Funding: The study was funded by the University of Edinburgh, which

had no direct role in the study. The corresponding author had full

independence from the funding source.

Competing interests: All authors havecompleted the Unified Competing

Interest form at www.icmje.org/coi_disclosure.pdf(available on requestfrom the corresponding author) and declare: support from the University

of Edinburgh for the submitted work; no financial relationships with any

organisations that might have an interest in the submitted work in the

previous 3 years; MD, SP-B, SJW, and OJG work as consultant

surgeons in one of the institutions included in this study, constituting a

non-financial interest that may be relevant to the submitted work.

Ethical approval: Study approval was granted by the National Services

Scotland Privacy Advisory Committee. No ethical approval was required.

Patient consent: None required.

Data sharing: No additional data available.

1 Finks JF, Osborne NH, Birkmeyer JD. Trends in hospital volume and operative mortality

for high-risk surgery. N Engl J Med2011;364:2128-37.

2 Chowdhury MM, Dagash H, Pierro A. A systematic review of the impact of volume ofsurgery and specialization on patient outcome. Br J Surg2007;94:145-61.

3 Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon

volume and operative mortality in the United States. N Engl J Med 2003;349:2117-27.

4 Gooiker GA, van Gijn W, Wouters MWJM, Post PN, van de Velde CJH, Tollenaar RAEM,

et al. Systematic review and meta-analysis of the volume-outcome relationship in

pancreatic surgery. Br J Surg2011;98:485-94.

5 Mayer EK, Bottle A, Darzi AW, Athanasiou T, Vale JA. The volume-mortality relation for

radical cystectomy in England: retrospective analysis of hospital episode statistics. BMJ

2010;340:c1128.

6 Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with

major inpatient surgery in Medicare patients. Ann Surg2009;250:1029-34.

7 Schilling PL,DimickJB, Birkmeyer JD.Prioritizing qualityimprovementin generalsurgery.

J Am Coll Surg2008;207:698-704.

8 Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical

relation between surgical volume and mortality. N Engl J Med 1979;301:1364-9.

9 Murphy MM, Ng SC, Simons JP, Csikesz NG, Shah SA, Tseng JF. Predictors of major

complications after laparoscopic cholecystectomy: surgeon, hospital, or patient? J Am

Coll Surg 2010;211:73-80.

10 Csikesz NG, Singla A, Murphy MM, Tseng JF, Shah SA. Surgeon volume metrics in

laparoscopic cholecystectomy.Dig Dis Sci

2009;55:2398-405.11 Von Elm E, Altman DG, Egger M, Pocock SJ, Gtzsche PC, Vandenbroucke JP. The

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)

statement: guidelines for reporting observational studies. Lancet2007;370:1453-7.

12 Li B, Evans D, Faris P, Dean S, Quan H. Risk adjustment performance of Charlson and

Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health

Serv Res2008;8:12.

13 Kim SH, Park S-J, Lee S, Lee WJ, Park J-W, Hong EK, et al. Various liver resections

using hanging maneuver by three glissons pedicles and three hepatic veins. Ann Surg

2007;245:201-5.

14 Austin PC, Rothwell DM, Tu JV. A comparison of statistical modeling strategies for

analyzing length of stay after CABG surgery. Health Serv Outcomes Res Methodol

2002;3:107-33.

15 King G, Tomz M, Wittenberg J. Making the most of statistical analyses: improving

interpretation and presentation. Am J Pol Sci 2000;44:341-55.

16 Imai K, King G, Lau O. Zelig: everyones statistical software. 2007. http://gking.harvard.

edu/zelig.

17 Imai K, King G, Lau O. Toward a common framework for statistical analysis and

development. J Computational Graphical Stat2008;17:892-913.

18 Khuri SF, Daley J, Henderson W, Hur K, Hossain M, Soybel D, et al. Relation of surgical

volume to outcome in eight common operations: results from the VA National SurgicalQuality Improvement Program. Ann Surg1999;230:414.

19 Buanes T, Mjland O, Waage A, Langeggen H, Holmboe J. A population-based survey

of biliary surgery in Norway. Surg Endosc1998;12:852-5.

20 McMahon AJ, Fischbacher CM, Frame SH, MacLeod M. Impact of laparoscopic

cholecystectomy: a population-based study. Lancet2000;356:1632-7.

21 Bateson MC. Gallstones and cholecystectomy in modern Britain. Postgrad Med J

2000;76:700-3.

22 Escarce JJ, Chen W, Schwartz JS. Falling cholecystectomy thresholds since the

introduction of laparoscopic cholecystectomy. JAMA 1995;273:1581.

23 Gurusamy K, Samraj K, Gluud C, Wilson E, Davidson BR. Meta-analysis of randomized

controlled trials on the safety and effectiveness of early versus delayed laparoscopic

cholecystectomy for acute cholecystitis. Br J Surg2009;97:141-50.

24 Harboe KM,BardramL. Nationwide qualityimprovementof cholecystectomy:resultsfrom

a national database. Int J Qual Health Care 2011;23:565-73.

25 Rosenmller M, Haapamki M, Nordin P, Stenlund H, Nilsson E. Cholecystectomy in

Sweden 2000-2003: a nationwide study on procedures, patient characteristics, and

mortality. BMC Gastroenterol2007;7:35.

26 David GG, Al-Sarira AA, Willmott S, Deakin M, Corless DJ, Slavin JP. Management of

acute gallbladder disease in England. Br J Surg2007;95:472-6.27 Flum DR, Cheadle A, Prela C, Dellinger EP, Chan L. Bile duct injury during

cholecystectomy and survival in medicare beneficiaries. JAMA 2003;290:2168.

Accepted: 08 April 2012

Cite this as: BMJ2012;344:e3330

This is an open-access article distributed under the terms of the Creative Commons

Attribution Non-commercial License, which permits use, distribution, and reproduction in

any medium, provided the original work is properly cited, the use is non commercial and

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Tables

Table 1| Patient characteristics by hospital volume. Data are no (%) unless otherwise stated

Hospital volumeHigh (n=18 425)Medium (n=20 534)Low (n=20 959)

54.0 (29-77)52.0 (28-74)54.0 (30-77)Age (years)*

3.0:13.5:13.2:1Male:female ratio

Admission type

12 381 (67.2)16 524 (80.5)17 374 (82.9)Elective

6044 (32.8)4010 (19.5)3585 (17.1)Non-elective

Diagnosis

14 694 (79.8)14 465 (70.4)15 924 (76.0)Cholelithiasis

2003 (10.9)4883 (23.8)4095 (19.5)Cholecystitis

535 (2.9)240 (1.2)174 (0.8)Acute pancreatitis

1193 (6.5)946 (4.6)766 (3.7)Other

Deprivation (SIMD score)

3379 (18.3)6637 (32.3)4006 (19.1)1 (high)

3894 (21.1)4616 (22.5)4995 (23.8)2

3468 (18.8)3447 (16.8)5336 (25.5)3

3637 (19.7)2855 (13.9)4234 (20.2)4

3965 (21.5)2921 (14.2)2257 (10.8)5 (low)

82 (0.4)58 (0.3)131 (0.6)Missing data

Morbidity

2339 (12.7)2577 (12.5)2505 (12.0)Charlson score >0

2695 (14.6)2973 (14.5)2982 (14.2)Elixhauser score >0

*Data are median (interquartile range).



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Table 2| Operative procedures associated with cholecystectomy. Data are no (%)

P*Hospital volume

High (n=18 425)Medium (n=20 534)Low (n=20 959)

Approach


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Table 3| Unadjusted outcomes after cholecystectomy. Data are no (%) unless stated otherwise

PHospital volume

High (n=18 425)Medium (n=20 534)Low (n=20 959)

0.0973 (0.40)112 (0.55)107 (0.51)Mortality*


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Table 4| Adjusted outcomes after cholecystectomy, by analysis

Length of stayReadmissionReoperationMortality

Cox

proportional

hazardsMultivariableUnivariableMultivariableUnivariableMultivariable*Univariable

P

HR(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95% CI)P

OR

(95% CI)


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Table 4 (continued)

Length of stayReadmissionReoperationMortality

Cox

proportional

hazardsMultivariableUnivariableMultivariableUnivariableMultivariable*Univariable

P

HR(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95%

CI)P

OR

(95% CI)P

OR

(95% CI)

Mixed effects hierarchical model (Hosmer-Lemeshow test, 2

11.276, df=8, P=0.187 on fixed components; area under the receiver operator characteristic curve,

c statistic 0.66).

Mixed effects hierarchical model (Hosmer-Lemeshow test, 2

7.24, df=8, P=0.511 on fixed components; area under the receiver operator characteristic curve, c

statistic 0.60).



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Table 5| Risk modelling for probability of death after cholecystectomy using simulations. Operation year for all examples was 2007

Example 7Example 6Example 5Example 4Example 3Example 2Example 1Risk factor

70707055-6955-6955-6955-69Age (years)

MaleFemaleFemaleFemaleFemaleFemaleFemaleSex

YesYesNoYesNoYesNoNon-electiveadmission

CholecystitisCholecystitisCholelithiasisCholecystitisCholelithiasisCholecystitisCholelithiasisDiagnosis

1111133Deprivation (SIMD

score)

4444400Morbidity (Charlson

score)

Probability of death (95% CI)

0.206 (0.132 to

0.298)

0.151 (0.094 to

0.226)

0.0153 (0.0086 to

0.0251)

0.048 (0.027 to

0.078)

0.0043 (0.0024 to

0.0072)

0.0088 (0.0056 to

0.0133)

0.00076 (0.00049 to

0.00113)

Low volume

0.199 (0.129 to

0.285)

0.146 (0.091 to

0.216)

0.0146 (0.0083 to

0.0238)

0.046 (0.026 to

0.073)

0.0041 (0.0023 to

0.0068)

0.0085 (0.0053 to

0.0128)

0.00073 (0.00047 to

0.00110)

Medium volume

0.147 (0.090 to

0.222)

0.106 (0.063 to

0.165)

0.0102 (0.0056 to

0.0170)

0.032 (0.018 to

0.054)

0.0029 (0.0015 to

0.0048)

0.0059 (0.0036 to

0.0090)

0.00051 (0.00032 to

0.00076)

High volume

Absolute risk difference (95% CI)

0.059 (0.014 to

0.110)

0.045 (0.010 to

0.087)

0.0051 (0.0011 to

0.0107)

0.016 (0.003 to

0.032)

0.0014 (0.0003 to

0.0031)

0.0030 (0.0007 to

0.0059)

0.00026 (0.00006 to

0.00051)

Low vhigh volume*

0.051 (0.007 to

0.024)

0.040 (0.005 to

0.079)

0.0044 (0.0006 to

0.0096)

0.013 (0.002 to

0.028)

0.0013 (0.0002 to

0.0027)

0.0026 (0.0003 to

0.0054)

0.00023 (0.00003 to

0.00047)

Medium vhigh

volume

Number needed to treat to harm (95% CI)

17 (9 to 74)22 (11 to 97)196 (94 to 908)64 (31 to 294)691 (325 to 3242)338 (171 to 1491)3871 (1963 to 17

118)

Low vhigh volume*

19 (10 to 143)25 (13 to 185)226 (104 to 1726)74 (35 to 557)798 (366 to 6216)387 (186 to 781)4431 (2120 to 34

681)

Medium vhigh

volume

*P=0.010.

P=0.023.



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Figures

Fig 1 Total number of cholecystectomies performed per year in 1998-2007

Fig 2 Mean number of cholecystectomies performed per year in 1998-2007, by hospital volume group

Fig 3 Mean annual hospital volume per institution. Each bar represents one of 37 hospitals in Scotland that performedcholecystectomies during the study period



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Fig 4 Risk modelling using simulation procedures. Left panel shows probability of death, reoperation, and readmission byhospital volume group for elective (example 1 in table 5) and non-elective (example 2 in table 5) cholecystectomy in patientsat standard risk (operation year 2007, age 55-69 years, female, SIMD score 3, Charlson score 0). Shaded area=95%confidence interval. Right panel shows probability of death, reoperation, and readmission in the elective setting by hospitalvolume group as a function of age, SIMD score, and Charlson score (operation year 2007, female, elective admission,cholelithiasis diagnosis).


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Harrison Et Al. - 2012 - Hospital Volume and Patient Outcomes After Cholecy

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